The reliability of the collaborative spectrum sensing
(CSS) can be severely decreased by spectrum sensing data
falsification (SSDF) attacks. In an SSDF attack, some malicious
users intentionally report incorrect local sensing results to the
fusion center (FC) and disrupt the global decision-making process. The present study introduces a new defense scheme called
attack-aware CSS (ACSS). The proposed method estimates attack
strength and applies it in the k−out−N rule to obtain the optimum value of k that minimizes the Bayes risk. The attack strength
is defined as the ratio of the number of malicious users to the total
number of users, which is equal to the probability that a specific
user is malicious.